Full text of this article is only available in PDF format.

Lauri Korhonen (email), Kari T. Korhonen, Pauline Stenberg, Matti Maltamo, Miina Rautiainen

Local models for forest canopy cover with beta regression

Korhonen L., Korhonen K. T., Stenberg P., Maltamo M., Rautiainen M. (2007). Local models for forest canopy cover with beta regression. Silva Fennica vol. 41 no. 4 article id 275. https://doi.org/10.14214/sf.275

Abstract

Accurate field measurement of the forest canopy cover is too laborious to be used in extensive forest inventories. A possible alternative to the separate canopy cover measurements is to utilize the correlations between the percent canopy cover and easier-to-measure forest variables, especially the basal area. A fairly new analysis technique, the beta regression, is specially designed for modelling percentages. As an extension to the generalized linear models, the beta regression takes into account the distribution of the model residuals, and uses a logistic link function to ensure logical predictions. In this study, the beta regression method was found to perform well in conifer dominated study area located in central Finland. The same model shape, with basal area, tree height and an additional predictor (Scots pine: site fertility, Norway spruce: percentage of hardwoods) as independent variables, produced good results for both pine and spruce dominated sites. The models had reasonably high pseudo R-squared values (pine: 0.91, spruce: 0.87) and low standard errors (pine: 6.3%, spruce: 5.9%) for the fitting data, and also performed well in a cross validation test. The models were also tested on separate test plots located in a different geographical area, where the prediction errors were slightly larger (pine: 8.8%, spruce: 7.4%). In pine plots, the model fit was further improved by introducing additional predictors such as stand age and density. This improved also the performance of the models in the cross validation test, but weakened the results for the external data set. Our results indicated that the beta regression method offers a noteworthy alternative to separate canopy cover measurements, especially if time is limited and the models can be applied in the same region where the modelling data were collected.

Keywords
beta regression; canopy cover; forest canopy

Author Info
  • Korhonen, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail lauri.korhonen@joensuu.fi (email)
  • Korhonen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail ktk@nn.fi
  • Stenberg, Univ. of Helsinki, Dept of Forest Resource Management, P.O. BOX 27, FI-00014 University of Helsinki, Finland E-mail ps@nn.fi
  • Maltamo, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail mm@nn.fi
  • Rautiainen, Univ. of Helsinki, Dept of Forest Resource Management, P.O. BOX 27, FI-00014 University of Helsinki, Finland E-mail mr@nn.fi

Received 15 May 2007 Accepted 4 October 2007 Published 31 December 2007

Views 5500

Available at https://doi.org/10.14214/sf.275 | Download PDF

Creative Commons License CC BY-SA 4.0

Register
Click this link to register to Silva Fennica.
Log in
If you are a registered user, log in to save your selected articles for later access.
Contents alert
Sign up to receive alerts of new content

Your selected articles
Your search results
Kangas A., Korhonen K. T. (1995) Generalizing sample tree information with semipa.. Silva Fennica vol. 29 no. 2 article id 5553
Korhonen K. T., (1993) Mixed estimation in calibration of volume functi.. Silva Fennica vol. 27 no. 4 article id 5520
Korhonen K. T., (1992) Calibration of upper diameter models in large sc.. Silva Fennica vol. 26 no. 4 article id 5491
Korhonen K. T., Maltamo M. (1991) The evaluation of forest inventory designs using.. Silva Fennica vol. 25 no. 2 article id 5444
Korhonen K. T., (2024) Corrigendum: Forests of Finland 2019–2023 and th.. Silva Fennica vol. 58 no. 5 article id 24068
Korhonen K. T., Räty M. et al. (2024) Forests of Finland 2019–2023 and their developme.. Silva Fennica vol. 58 no. 5 article id 24045
Korhonen K. T., Ahola A. et al. (2021) Forests of Finland 2014–2018 and their developme.. Silva Fennica vol. 55 no. 5 article id 10662
Haara A., Matala J. et al. (2021) Economic effects of grouse-friendly forest manag.. Silva Fennica vol. 55 no. 3 article id 10468
Nevalainen S., Matala J. et al. (2016) Moose damage in National Forest Inventories (198.. Silva Fennica vol. 50 no. 2 article id 1410
Tuominen S., Pitkänen J. et al. (2014) NFI plots as complementary reference data in for.. Silva Fennica vol. 48 no. 2 article id 983
Korhonen L., Korhonen K. T. et al. (2007) Local models for forest canopy cover with beta r.. Silva Fennica vol. 41 no. 4 article id 275
Korhonen L., Korhonen K. T. et al. (2006) Estimation of forest canopy cover: a comparison .. Silva Fennica vol. 40 no. 4 article id 315
Tomppo E., Korhonen K. T. et al. (2001) Multi-source inventory of the forests of the Heb.. Silva Fennica vol. 35 no. 3 article id 587
Nuutinen T., Hirvelä H. et al. (2000) The role of peatlands in Finnish wood production.. Silva Fennica vol. 34 no. 2 article id 636